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Record W3157226959 · doi:10.1558/lst.19037

Application of a SCOBA in Educational Praxis of L2 Written Argumentative Discourse

2021· article· en· W3157226959 on OpenAlexaff
Ali Hadidi

Bibliographic record

VenueLanguage and Sociocultural Theory · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsYork University
Fundersnot available
KeywordsArgumentativeSchema (genetic algorithms)AppropriationCognitionPraxisLinguisticsPsychologyArgumentation theoryComputer scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

The purpose of this study was to examine if and the way in which a central argumentative discourse schema, as a cognitive tool, was appropriated by an English language learner. There has been little research on the development of L2 written argumentative discourse after a period of instruction and no study, to my knowledge, examining and detailing a systematic pedagogy for L2 learners. Grounded in both C-BLI (concept-based language instruction) and cognitive-process theory of writing (Bereiter and Scardamlaia, 1987), the present study details the appropriation of a central Toulmin (1958/2003) SCOBA, ‘schema for complete orientating basis of an action,’ (Gal’perin, 1989: 70) to mediate the cognitive processes leading to the production of texts that feature argumentative discourse features. The central Toulmin SCOBA and the text generation artifacts that were (co-) constructed during C-BLI will be examined and evidence will be provided for the effectiveness of the SCOBA. There will be a theoretical and empirical discussion of how the SCOBA and its related artifacts made the-rule-of thumb (Negueruela, 2003) and amorphous idea (Vygotsky, 1986) of thesis-support scientific and discrete. In order to guide the teaching-learning of written argumentative discourse, the cognitive processes of writing were conceptualized as mental actions (Gal’perin, 1989). The findings indicate that the learner’s cognitive processes of composing and the quality of his texts improved during and after instruction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.349
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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